Big Data and Its Role in Facilitating the Visualization of Financial Analytics

Big Data and Its Role in Facilitating the Visualization of Financial Analytics

Iman Raeesi Vanani (Allameh Tabataba'i University, Iran) and Maziar Shiraj Kheiri (Allameh Tabataba'i University, Iran)
DOI: 10.4018/978-1-5225-3142-5.ch024

Abstract

The business use of data analytics is growing rapidly in the accounting environment. Similar to many new systems that involve accounting information, data analytics has fundamentally changed task based processes particularly those tasks that provide inference, prediction and assurance to decision makers. Big Data analytics is the process of inspecting, cleaning, transforming, and modeling Big Data to discover and communicate useful information and patterns, suggest conclusions, and support decision making. Big Data now pervades every sector and function of the global economy. These essays focus on the uses and challenges of Big Data in accounting (measurement) and auditing (assurance). The objective of this chapter is to examine how Big Data analytics will impact the accounting and auditing environment. This is important to practitioners as well as academics because they will be using data analytics in accounting and auditing tasks and will need to have an in-depth familiarity with financial analytics to effectively accomplish these tasks and make effective and efficient decisions.
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Introduction To Big Data In Accounting And Auditing

Accounting records are ‘‘records of financial transactions, or of events expressed in monetary terms, made for the purposes of accounting’’ (Accounting Dictionary, 2014). Although such records are historically physical, they are now almost exclusively digitized (Warren et al., 2015). For example, in 2000, about 25 percent of all stored information was digital whereas currently more than 98 percent of this information is electronically stored in databases (Cukier & Mayer-Schönberger, 2013).

Key Terms in this Chapter

Accounting Record: Accounting records are historically financial in nature and consist of data aggregated and used to prepare financial statements for internal (e.g., Management) and external (e.g. Investors and Creditors) users ( Warren et al., 2015 ).

Data aggregation: Data Aggregation is any process in which information is gathered and expressed in a summary form for purposes such as statistical analysis. A common aggregation purpose is to get more information about particular groups based on specific variables such as Age, Profession or Income.

Responsibility: Responsibility Accounting is a reporting system that compiles Revenue, Cost and profit information at the level of those individual managers most directly responsible for them. The intent is to provide this information to those people most able to act upon it as well as to judge their performance. 1.Resources available to a firm, 2. The means employed to finance those resources, and 3. The results achieved through their use.

Assurance: An A ssurance Service is an independent professional service that improves the quality of information for decision makers. Assurance services can be done by CPAs or by a variety of other professionals (Arense et al., 2014 AU76: The in-text citation "Arense et al., 2014" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

Big Data: Big Data includes datasets that are too large and complex to manipulate or interrogate with standard methods or tools. It is characterized by ‘‘three Vs’’: Volume, Velocity and Variety ( Cao et al., 2015 ).

Financial Analytics: Financial Analytics is the creation of ad hoc analysis to answer specific business questions and forecast possible future financial scenarios. Analytical procedures are defined by auditing standards as evaluations of financial information made by a study of plausible relationships among financial and non-financial data involving comparisons of recorded amounts to expectations developed by the Auditor (Arense et al., 2014 AU80: The in-text citation "Arense et al., 2014" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

Auditing: Auditing is the accumulation and evaluation of evidence about information to determine and report on the degree of correspondence between the information and established criteria. In other words, Auditing is determining whether recorded information properly reflects the economic events that occurred during the Accounting period (Arense et al., 2014 AU77: The in-text citation "Arense et al., 2014" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

Business Intelligence: Business Intelligence (BI) is defined as ‘‘the set of techniques and tools for the transformation of raw data into meaningful and useful information for Business Analysis purposes”. BI technologies are capable of handling large amounts of unstructured data to help identify, develop, and otherwise create new strategic business opportunities ( Vasarhelyi et al., 2015 ).

Evidence: Evidence is any information used by the Auditor to determine whether the information being audited is stated in accordance with the established criteria (Arense et al., 2014 AU79: The in-text citation "Arense et al., 2014" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

Data Analytics: Data Analytics includes tools that leverage current technologies to extract and analyze information (Jordan, 2013 AU78: The in-text citation "Jordan, 2013" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

Accountability: As suggested earlier, Accountability occurs when one's behavior could fall under scrutiny of another individual. In the rater Accountability literature – consistent with the broad literature into the effect of Accountability on judgements and behaviors –the effect of Accountability is predominantly studied using experimental designs (Harari & Rudolph, 2016 AU75: The in-text citation "Harari & Rudolph, 2016" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

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